TestMachine · Security for the agentic age

Same models. Same tools. The edge is how much you can let go.

Capability is converging. What still differs is how much of it you can put to work without a person watching every step. You set the intent — TestMachine turns it into guardrails, enforces them on every action, and tells you the moment an agent drifts.

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Every dot is an agent action running inside its mandate. Push one out — or click to apply pressure.

The autonomy gap

The autonomy gap is where the value is.

62%of financial-services organizations already use AI agents
5%of those agent users let agents act independently on critical actions

Every approval gate is a queue, and an agent waiting on a person is capacity you paid for, sitting idle. Autonomy does not make a model smarter. It lets you actually use the intelligence you already have.

Source: Cloud Security Alliance, State of Cloud and AI for Financial Services 2026 (340 respondents).

Human-in-the-loop does not scale — and it is not as safe as it looks.

It gets tired.

People are poor at sustained vigilance over streams that are almost always fine. By the fortieth approval of the day, the reviewer is matching the shape of the dialog box, not reading it.

More approval can mean less safety.

Model reviewers realistically, with attention that degrades under load, and safety follows an inverted U: past a certain escalation rate, more human checks make the system less safe.

The gate itself is attackable.

“Loopjacking”: what a human approved is not what runs. Tool configurations change after approval; command arguments change between approval and execution.

None of this means humans should leave. It means humans are in the wrong place.

How it works

Stop writing guardrails. Start writing intent.

Humans set policy, goals and acceptable outcomes. A system turns that intent into guardrails, enforces them, and tells you the moment an agent drifts.

01
You state intent
What the agent is for, what counts as success, and what must never happen. Plain language, owned by your team.
02
We derive the controls
Analysis of the agent’s code and runtime maps which untrusted inputs can reach which sensitive actions, and classifies each action by blast radius.
03
Traps where abuse would show
Canary credentials that should never be used, and tripwires on arguments and budgets, turn a hijacked agent into a loud signal instead of a silent breach.
04
Humans see only what matters
Routine work runs. Out-of-policy behavior is blocked or escalated, with the path that led to it — and every control traces back to the policy line it came from.

Your intentpolicy · goals · outcomes

Reachabilitysource → sink

Blast radiusper action

The recordevery block, escalation, tripwire

Trapscanaries & tripwires

The foundation

We built the attacker first… and it works

Azimuth, our attack agent, explores systems, reasons about attack paths and validates them through execution. That offensive understanding of how complex systems fail is how we derive controls: we know which inputs can reach which actions because finding those paths is what we do.